Photoplethysmogram, Machine learning, Biometric identification, Pulse wave


Background. In recent years, the development of an automated identification process using biometric authentication has been observed, which has a high level of protection, since it allows you to evaluate the physical parameters and characteristics of a particular person. Such access control is more reliable since identifiers cannot be transferred to third parties or duplicated to bypass security systems. Over the past decades, a significant number of systems with biometric identification has been developed, but systems with identification according to the characteristics of the photoplethysmogram still receive little attention. The main task of biometric personality identification using photoplethysmogram is the search and implementation of machine learning methods to determine their belonging to a particular patient.

Objective. The purpose of the paper is to develop an algorithm for distinguishing pulse wave iterations using the calculation of the temporal characteristics of photoplethysmograms, such as the maximum amplitude value, variance, mean absolute deviation, Wilson amplitude and the total sum of signal amplitude values.

Methods. Based on the study of the temporal characteristics of the photoplethysmogram, an algorithm for distinguishing pulse wave iterations is created, which can be used for further biometric identification of a person using machine-learning methods.

Results. The results can be used for further development of automated access control and management systems using biometric identification.

Conclusions. Known methods of biometric identification are usually based on the static parameters of a person (the structure of the cornea of the eye, palm, fingerprints, geometry of the auricle, etc.), but have a low level of protection, since using special equipment you can create a copy of the biometric key. Therefore, today, the use of methods based on the parameters of dynamic biometric identification (plethysmogram, cardiogram and others) provides the highest degree of protection, but requires a more accurate software device to isolate and determine common symptoms. The proposed approach to calculating individual parameters of the photoplethysmogram with the aim of their subsequent classification by machine learning methods may be an acceptable solution for patient biometric identification systems.

Author Biographies

Iryna O. Yakovenko, Igor Sikorsky Kyiv Polytechnic Institute

Ірина Олександрівна Яковенко

Kostiantyn P. Vonsevych, Igor Sikorsky Kyiv Polytechnic Institute

Костянтин Петрович Вонсевич

Illia Ye. Hreben, Igor Sikorsky Kyiv Polytechnic Institute

Ілля Євгенович Гребень


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